def makegrid(self, nx, ny, returnxy=False):
"""
return arrays of shape (ny,nx) containing lon,lat coordinates of
an equally spaced native projection grid.
if returnxy=True, the x,y values of the grid are returned also.
"""
dx = (self.urcrnrx - self.llcrnrx) / (nx - 1)
dy = (self.urcrnry - self.llcrnry) / (ny - 1)
x = self.llcrnrx + dx * N.indices((ny, nx))[1, :, :]
y = self.llcrnry + dy * N.indices((ny, nx))[0, :, :]
lons, lats = self(x, y, inverse=True)
if returnxy:
return lons, lats, x, y
else:
return lons, lats
def makegrid(self, nx, ny, returnxy=False):
"""
return arrays of shape (ny,nx) containing lon,lat coordinates of
an equally spaced native projection grid.
if returnxy=True, the x,y values of the grid are returned also.
"""
dx = (self.urcrnrx - self.llcrnrx) / (nx - 1)
dy = (self.urcrnry - self.llcrnry) / (ny - 1)
x = self.llcrnrx + dx * N.indices((ny, nx))[1, :, :]
y = self.llcrnry + dy * N.indices((ny, nx))[0, :, :]
lons, lats = self(x, y, inverse=True)
if returnxy:
return lons, lats, x, y
else:
return lons, lats
def main():
import pylab
# Create the gaussian data
Xin, Yin = pylab.mgrid[0:201, 0:201]
data = gaussian(3, 100, 100, 20, 40)(Xin, Yin) + np.random.random(Xin.shape)
pylab.matshow(data, cmap='gist_rainbow')
params = fitgaussian(data)
fit = gaussian(*params)
pylab.contour(fit(*pylab.indices(data.shape)), cmap='copper')
ax = pylab.gca()
(height, x, y, width_x, width_y) = params
pylab.text(0.95, 0.05, """
x : %.1f
y : %.1f
width_x : %.1f
width_y : %.1f""" %(x, y, width_x, width_y),
fontsize=16, horizontalalignment='right',
verticalalignment='bottom', transform=ax.transAxes)
pylab.show()
def main():
#from pylab import *
import pylab
# Create the gaussian data
Xin, Yin = pylab.mgrid[0:201, 0:201]
data = gaussian(3, 100, 100, 20, 40)(Xin, Yin) + np.random.random(
Xin.shape)
pylab.matshow(data, cmap='gist_rainbow')
params = fitgaussian(data)
fit = gaussian(*params)
pylab.contour(fit(*pylab.indices(data.shape)), cmap='copper')
ax = pylab.gca()
(height, x, y, width_x, width_y) = params
pylab.text(0.95,
0.05,
"""
x : %.1f
y : %.1f
width_x : %.1f
width_y : %.1f""" % (x, y, width_x, width_y),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
pylab.show()
def fitgaussian(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution found by a fit"""
params = moments(data)
errorfunction = lambda p: ravel(gaussian(*p)(*indices(data.shape)) - data)
p, success = optimize.leastsq(errorfunction, params)
return p
def wrap_collapse_adaptive(filename,outprefix,redo='no',nsig=5,nrsig=2,doplot=True):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv,v0,p3 = fitsfile[0].header['CD3_3'],fitsfile[0].header['CRVAL3'],fitsfile[0].header['CRPIX3']
dr,r0,p1 = fitsfile[0].header['CD1_1'],fitsfile[0].header['CRVAL1'],fitsfile[0].header['CRPIX1']
dd,d0,p2 = fitsfile[0].header['CD2_2'],fitsfile[0].header['CRVAL2'],fitsfile[0].header['CRPIX2']
xtora = lambda x: (x-p1+1)*dr+r0 # convert pixel coordinates to RA/Dec/Velocity
ytodec = lambda y: (y-p2+1)*dd+d0
vconv = lambda v: (v-p3+1)*dv+v0
cube = fitsfile[0].data
cube = where(numpy.isnan(cube),0,cube)
if redo=='no':
adaptB = asarray(adaptive_collapse_gaussfit(cube,axis=0,prefix=outprefix+'_triple',
nsig=nsig,nrsig=nrsig,vconv=vconv,xtora=xtora,ytodec=ytodec,doplot=doplot))
adaptB[numpy.isnan(adaptB)] = 0
pickle.dump(adaptB,open('%s_adaptB.pysav' % outprefix,'w'))
else:
adaptB = pickle.load(open('%s_adaptB.pysav' % outprefix,'r'))
db = adaptB
gcd = double_gaussian(db[3],db[4],db[0],db[1],db[5],db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_adaptgausscube.fits' % outprefix,clobber=True)
gcd[numpy.isnan(gcd)] = 0
adaptResids = cube-gcd
fitsfile[0].data = adaptResids
fitsfile.writeto('%s_adaptgaussresids.fits' % outprefix,clobber=True)
#adaptB[4] = (adaptB[4]-v0) / dv + p3-1
#adaptB[3] = (adaptB[3]-v0) / dv + p3-1
adaptB[4] = (adaptB[4]-p3+1) * dv + v0
adaptB[3] = (adaptB[3]-p3+1) * dv + v0
fitsfile[0].data = asarray(adaptB)
fitsfile.writeto('%s_adaptgausspars.fits' % outprefix,clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
adaptResids[numpy.isnan(adaptResids)] = 0
totalDResids = adaptResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_adaptgauss_totalresids.fits' % outprefix,clobber=True)
return adaptB
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments
"""
total = data.sum()
bias = data.mean()
X, Y = indices(data.shape)
x = (X * data).sum() / total
y = (Y * data).sum() / total
col = data[:, int(y)]
width_x = sqrt(abs((arange(col.size) - y)**2 * col).sum() / col.sum())
row = data[int(x), :]
width_y = sqrt(abs((arange(row.size) - x)**2 * row).sum() / row.sum())
height = data.max()
return bias, height, x, y, width_x, width_y
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments
"""
#total = data.sum()
#bias=data.mean()
bias = np.min(data) # revised by Kai to make faint object detection easier
data_this = data - bias
total = data_this.sum()
X, Y = indices(data.shape)
x = (X * data_this).sum() / total
y = (Y * data_this).sum() / total
col = data_this[:, int(y)]
width_x = sqrt(abs((arange(col.size) - y)**2 * col).sum() / col.sum())
row = data_this[int(x), :]
width_y = sqrt(abs((arange(row.size) - x)**2 * row).sum() / row.sum())
height = data_this.max()
return bias, height, x, y, width_x, width_y
def wrap_collapse_gauss(filename,outprefix,redo='no'):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv,v0,p3 = fitsfile[0].header['CD3_3'],fitsfile[0].header['CRVAL3'],fitsfile[0].header['CRPIX3']
cube = fitsfile[0].data
cube = where(numpy.isnan(cube),0,cube)
if redo=='no':
doubleB = asarray(collapse_double_gaussfit(cube,axis=0))
doubleB[numpy.isnan(doubleB)] = 0
pickle.dump(doubleB,open('%s_doubleB.pysav' % outprefix,'w'))
else:
doubleB = pickle.load(open('%s_doubleB.pysav' % outprefix,'r'))
db = doubleB
gcd = double_gaussian(db[3],db[4],db[0],db[1],db[5],db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_doublegausscube.fits' % outprefix,clobber=True)
gcd[numpy.isnan(gcd)] = 0
doubleResids = cube-gcd
fitsfile[0].data = doubleResids
fitsfile.writeto('%s_doublegaussresids.fits' % outprefix,clobber=True)
#doubleB[4] = (doubleB[4]-v0) / dv + p3-1
#doubleB[3] = (doubleB[3]-v0) / dv + p3-1
doubleB[4] = (doubleB[4]-p3+1) * dv + v0
doubleB[3] = (doubleB[3]-p3+1) * dv + v0
fitsfile[0].data = asarray(doubleB)
fitsfile.writeto('%s_doublegausspars.fits' % outprefix,clobber=True)
if redo=='no':
singleB = asarray(collapse_gaussfit(cube,axis=0))
pickle.dump(singleB,open('%s_singleB.pysav' % outprefix,'w'))
else:
singleB = pickle.load(open('%s_singleB.pysav' % outprefix,'r'))
gc = gaussian(singleB[1],singleB[0],singleB[2])(indices(cube.shape)[0])
singleB[1] = (singleB[1]-p3+1) * dv + v0
fitsfile[0].data = gc
fitsfile.writeto('%s_singlegausscube.fits' % outprefix,clobber=True)
gc[numpy.isnan(gc)]=0
singleResids = cube-gc
fitsfile[0].data = singleResids
fitsfile.writeto('%s_singlegaussresids.fits' % outprefix,clobber=True)
fitsfile[0].data = asarray(singleB)
fitsfile.writeto('%s_singlegausspars.fits' % outprefix,clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
doubleResids[numpy.isnan(doubleResids)] = 0
totalDResids = doubleResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_doublegauss_totalresids.fits' % outprefix,clobber=True)
singleResids[numpy.isnan(singleResids)] = 0
totalSResids = singleResids.sum(axis=0)
fitsfile[0].data = totalSResids
fitsfile.writeto('%s_singlegauss_totalresids.fits' % outprefix,clobber=True)
return singleB,doubleB
def drawmeridians(self,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm').
fontsize - font size in points for labels (default 12).
"""
# get current axes instance.
ax = pylab.gca()
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry-self.llcrnry)/100./self.aspect
xoffset = (self.urcrnrx-self.llcrnrx)/100.
if self.projection not in ['merc','cyl']:
lats = pylab.arange(-latmax,latmax+1).astype('f')
else:
lats = pylab.arange(-90,91).astype('f')
xdelta = 0.1*(self.xmax-self.xmin)
ydelta = 0.1*(self.ymax-self.ymin)
for merid in meridians:
lons = merid*pylab.ones(len(lats),'f')
x,y = self(lons,lats)
# remove points outside domain.
testx = pylab.logical_and(x>=self.xmin-xdelta,x<=self.xmax+xdelta)
x = pylab.compress(testx, x)
y = pylab.compress(testx, y)
testy = pylab.logical_and(y>=self.ymin-ydelta,y<=self.ymax+ydelta)
x = pylab.compress(testy, x)
y = pylab.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:]-x[0:-1])**2
yd = (y[1:]-y[0:-1])**2
dist = pylab.sqrt(xd+yd)
split = dist > 500000.
if pylab.asum(split) and self.projection not in ['merc','cyl']:
ind = (pylab.compress(split,pylab.squeeze(split*pylab.indices(xd.shape)))+1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x,y in zip(xl,yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,y,linewidth=linewidth,linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01; dy = 0.01
else:
dx = 1000; dy = 1000
for dolab,side in zip(labels,['l','r','t','b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl','merc'] and side in ['l','r']: continue
if side in ['l','r']:
nmax = int((self.ymax-self.ymin)/dy+1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry-dy*pylab.arange(nmax)
else:
yy = self.llcrnry+dy*pylab.arange(nmax)
if side == 'l':
lons,lats = self(self.llcrnrx*pylab.ones(yy.shape,'f'),yy,inverse=True)
else:
lons,lats = self(self.urcrnrx*pylab.ones(yy.shape,'f'),yy,inverse=True)
lons = pylab.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
else:
nmax = int((self.xmax-self.xmin)/dx+1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx-dx*pylab.arange(nmax)
else:
xx = self.llcrnrx+dx*pylab.arange(nmax)
if side == 'b':
lons,lats = self(xx,self.llcrnry*pylab.ones(xx.shape,'f'),inverse=True)
else:
lons,lats = self(xx,self.urcrnry*pylab.ones(xx.shape,'f'),inverse=True)
lons = pylab.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
for lon in meridians:
if lon<0: lon=lon+360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon*10))
except:
nl = -1
try:
nr = len(lons)-lons[::-1].index(int(lon*10))-1
except:
nr = -1
if lon>180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$'%(font,pylab.fabs(lon-360))
elif lon<180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$'%(font,lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$'%(font,lon)
# meridians can intersect each map edge twice.
for i,n in enumerate([nl,nr]):
lat = lats[n]/10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc','cyl'] and lat > latmax: continue
# don't bother if close to the first label.
if i and abs(nr-nl) < 100: continue
if n >= 0:
if side == 'l':
pylab.text(self.llcrnrx-xoffset,yy[n],lonlab,horizontalalignment='right',verticalalignment='center',fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx+xoffset,yy[n],lonlab,horizontalalignment='left',verticalalignment='center',fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],self.llcrnry-yoffset,lonlab,horizontalalignment='center',verticalalignment='top',fontsize=fontsize)
else:
pylab.text(xx[n],self.urcrnry+yoffset,lonlab,horizontalalignment='center',verticalalignment='bottom',fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
# set axes limits to fit map region.
self.set_axes_limits()
def double_gerr(xarr):
return lambda p:xarr-double_gaussian(*p)(*indices(xarr.shape))
def gerr(xarr):
return lambda p: xarr - gaussian(*p)(*indices(xarr.shape))
def wrap_collapse_adaptive(filename,
outprefix,
redo='no',
nsig=5,
nrsig=2,
doplot=True):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv, v0, p3 = fitsfile[0].header['CD3_3'], fitsfile[0].header[
'CRVAL3'], fitsfile[0].header['CRPIX3']
dr, r0, p1 = fitsfile[0].header['CD1_1'], fitsfile[0].header[
'CRVAL1'], fitsfile[0].header['CRPIX1']
dd, d0, p2 = fitsfile[0].header['CD2_2'], fitsfile[0].header[
'CRVAL2'], fitsfile[0].header['CRPIX2']
xtora = lambda x: (
x - p1 + 1) * dr + r0 # convert pixel coordinates to RA/Dec/Velocity
ytodec = lambda y: (y - p2 + 1) * dd + d0
vconv = lambda v: (v - p3 + 1) * dv + v0
cube = fitsfile[0].data
cube = where(numpy.isnan(cube), 0, cube)
if redo == 'no':
adaptB = asarray(
adaptive_collapse_gaussfit(cube,
axis=0,
prefix=outprefix + '_triple',
nsig=nsig,
nrsig=nrsig,
vconv=vconv,
xtora=xtora,
ytodec=ytodec,
doplot=doplot))
adaptB[numpy.isnan(adaptB)] = 0
pickle.dump(adaptB, open('%s_adaptB.pysav' % outprefix, 'w'))
else:
adaptB = pickle.load(open('%s_adaptB.pysav' % outprefix, 'r'))
db = adaptB
gcd = double_gaussian(db[3], db[4], db[0], db[1], db[5],
db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_adaptgausscube.fits' % outprefix, clobber=True)
gcd[numpy.isnan(gcd)] = 0
adaptResids = cube - gcd
fitsfile[0].data = adaptResids
fitsfile.writeto('%s_adaptgaussresids.fits' % outprefix, clobber=True)
#adaptB[4] = (adaptB[4]-v0) / dv + p3-1
#adaptB[3] = (adaptB[3]-v0) / dv + p3-1
adaptB[4] = (adaptB[4] - p3 + 1) * dv + v0
adaptB[3] = (adaptB[3] - p3 + 1) * dv + v0
fitsfile[0].data = asarray(adaptB)
fitsfile.writeto('%s_adaptgausspars.fits' % outprefix, clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
adaptResids[numpy.isnan(adaptResids)] = 0
totalDResids = adaptResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_adaptgauss_totalresids.fits' % outprefix,
clobber=True)
return adaptB
def wrap_collapse_gauss(filename, outprefix, redo='no'):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv, v0, p3 = fitsfile[0].header['CD3_3'], fitsfile[0].header[
'CRVAL3'], fitsfile[0].header['CRPIX3']
cube = fitsfile[0].data
cube = where(numpy.isnan(cube), 0, cube)
if redo == 'no':
doubleB = asarray(collapse_double_gaussfit(cube, axis=0))
doubleB[numpy.isnan(doubleB)] = 0
pickle.dump(doubleB, open('%s_doubleB.pysav' % outprefix, 'w'))
else:
doubleB = pickle.load(open('%s_doubleB.pysav' % outprefix, 'r'))
db = doubleB
gcd = double_gaussian(db[3], db[4], db[0], db[1], db[5],
db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_doublegausscube.fits' % outprefix, clobber=True)
gcd[numpy.isnan(gcd)] = 0
doubleResids = cube - gcd
fitsfile[0].data = doubleResids
fitsfile.writeto('%s_doublegaussresids.fits' % outprefix, clobber=True)
#doubleB[4] = (doubleB[4]-v0) / dv + p3-1
#doubleB[3] = (doubleB[3]-v0) / dv + p3-1
doubleB[4] = (doubleB[4] - p3 + 1) * dv + v0
doubleB[3] = (doubleB[3] - p3 + 1) * dv + v0
fitsfile[0].data = asarray(doubleB)
fitsfile.writeto('%s_doublegausspars.fits' % outprefix, clobber=True)
if redo == 'no':
singleB = asarray(collapse_gaussfit(cube, axis=0))
pickle.dump(singleB, open('%s_singleB.pysav' % outprefix, 'w'))
else:
singleB = pickle.load(open('%s_singleB.pysav' % outprefix, 'r'))
gc = gaussian(singleB[1], singleB[0], singleB[2])(indices(cube.shape)[0])
singleB[1] = (singleB[1] - p3 + 1) * dv + v0
fitsfile[0].data = gc
fitsfile.writeto('%s_singlegausscube.fits' % outprefix, clobber=True)
gc[numpy.isnan(gc)] = 0
singleResids = cube - gc
fitsfile[0].data = singleResids
fitsfile.writeto('%s_singlegaussresids.fits' % outprefix, clobber=True)
fitsfile[0].data = asarray(singleB)
fitsfile.writeto('%s_singlegausspars.fits' % outprefix, clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
doubleResids[numpy.isnan(doubleResids)] = 0
totalDResids = doubleResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_doublegauss_totalresids.fits' % outprefix,
clobber=True)
singleResids[numpy.isnan(singleResids)] = 0
totalSResids = singleResids.sum(axis=0)
fitsfile[0].data = totalSResids
fitsfile.writeto('%s_singlegauss_totalresids.fits' % outprefix,
clobber=True)
return singleB, doubleB
def drawmeridians(self,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm').
fontsize - font size in points for labels (default 12).
"""
# get current axes instance.
ax = pylab.gca()
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry-self.llcrnry)/100./self.aspect
xoffset = (self.urcrnrx-self.llcrnrx)/100.
if self.projection not in ['merc','cyl']:
lats = pylab.arange(-latmax,latmax+1).astype('f')
else:
lats = pylab.arange(-90,91).astype('f')
xdelta = 0.1*(self.xmax-self.xmin)
ydelta = 0.1*(self.ymax-self.ymin)
for merid in meridians:
lons = merid*pylab.ones(len(lats),'f')
x,y = self(lons,lats)
# remove points outside domain.
testx = pylab.logical_and(x>=self.xmin-xdelta,x<=self.xmax+xdelta)
x = pylab.compress(testx, x)
y = pylab.compress(testx, y)
testy = pylab.logical_and(y>=self.ymin-ydelta,y<=self.ymax+ydelta)
x = pylab.compress(testy, x)
y = pylab.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:]-x[0:-1])**2
yd = (y[1:]-y[0:-1])**2
dist = pylab.sqrt(xd+yd)
split = dist > 500000.
if pylab.asum(split) and self.projection not in ['merc','cyl']:
ind = (pylab.compress(split,pylab.squeeze(split*pylab.indices(xd.shape)))+1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x,y in zip(xl,yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,y,linewidth=linewidth,linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01; dy = 0.01
else:
dx = 1000; dy = 1000
for dolab,side in zip(labels,['l','r','t','b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl','merc'] and side in ['l','r']: continue
if side in ['l','r']:
nmax = int((self.ymax-self.ymin)/dy+1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry-dy*pylab.arange(nmax)
else:
yy = self.llcrnry+dy*pylab.arange(nmax)
if side == 'l':
lons,lats = self(self.llcrnrx*pylab.ones(yy.shape,'f'),yy,inverse=True)
else:
lons,lats = self(self.urcrnrx*pylab.ones(yy.shape,'f'),yy,inverse=True)
lons = pylab.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
else:
nmax = int((self.xmax-self.xmin)/dx+1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx-dx*pylab.arange(nmax)
else:
xx = self.llcrnrx+dx*pylab.arange(nmax)
if side == 'b':
lons,lats = self(xx,self.llcrnry*pylab.ones(xx.shape,'f'),inverse=True)
else:
lons,lats = self(xx,self.urcrnry*pylab.ones(xx.shape,'f'),inverse=True)
lons = pylab.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
for lon in meridians:
if lon<0: lon=lon+360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon*10))
except:
nl = -1
try:
nr = len(lons)-lons[::-1].index(int(lon*10))-1
except:
nr = -1
if lon>180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$'%(font,pylab.fabs(lon-360))
elif lon<180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$'%(font,lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$'%(font,lon)
# meridians can intersect each map edge twice.
for i,n in enumerate([nl,nr]):
lat = lats[n]/10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc','cyl'] and lat > latmax: continue
# don't bother if close to the first label.
if i and abs(nr-nl) < 100: continue
if n >= 0:
if side == 'l':
pylab.text(self.llcrnrx-xoffset,yy[n],lonlab,horizontalalignment='right',verticalalignment='center',fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx+xoffset,yy[n],lonlab,horizontalalignment='left',verticalalignment='center',fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],self.llcrnry-yoffset,lonlab,horizontalalignment='center',verticalalignment='top',fontsize=fontsize)
else:
pylab.text(xx[n],self.urcrnry+yoffset,lonlab,horizontalalignment='center',verticalalignment='bottom',fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
# set axes limits to fit map region.
self.set_axes_limits()
def gerr(xarr):
return lambda p:xarr-gaussian(*p)(*indices(xarr.shape))
def double_gerr(xarr):
return lambda p: xarr - double_gaussian(*p)(*indices(xarr.shape))
def triple_gerr(xarr):
return lambda p:xarr-triple_gaussian(*p)(*indices(xarr.shape))
def triple_gerr(xarr):
return lambda p: xarr - triple_gaussian(*p)(*indices(xarr.shape))
img.show()
return img
img = cropImage()
# read the cropped image directly into a numpy array
imarray = numpy.array(img)
print("Numpy array shape:", imarray.shape)
print("Cropped image size", img.size)
# Create the gaussian data
data = imarray
pylab.matshow(data, cmap='gist_rainbow')
params = fg.fitgaussian(data)
fit = fg.gaussian(*params)
pylab.contour(fit(*pylab.indices(data.shape)), cmap='copper')
ax = pylab.gca()
(height, x, y, width_x, width_y) = params
legend = """
x : {:.1f}
y : {:.1f}
width_x : {:.1f}
width_y : {:.1f}""".format(x, y, width_x, width_y)
pylab.text(x = 0.95,
y = 0.05,
s = legend,
fontsize = 16,
horizontalalignment = 'right',
verticalalignment = 'bottom',
transform = ax.transAxes)
# removed 'uranus_070927_ob7-8_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_070927_ob9-0_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_091210_ob1-2_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_091210_ob3-4_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_091210_ob5-6_mask_v2.0_0pca_coalign_map10.fits',
# removed 'uranus_091210_ob7-8_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091216_ob8-9_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091219_o15-6_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091220_ob1-2_mask_v2.0_0pca_coalign_map10.fits',
'uranus_091224_o10-9_mask_v2.0_0pca_coalign_map10.fits',
]
if sample == 'notmars':
#filelist = [ a for a in filelist if a.find('mars')==-1 ]
#filelist = filelist[:4]
imstack = pylab.zeros([300, 300, len(filelist)])
xx, yy = pylab.indices([300, 300])
elif sample == 'dec2011':
filelist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
) for a in b
]
elif sample == 'dec2011notmars':
filelist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
) for a in b if 'mars' not in a
]
marslist = [
a for b in readcol(
'/Users/adam/work/bgps_pipeline/plotting/lists/pointsource_ds2_13pca_default.list'
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary()
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(p.arange(0,360,30))
map.drawparallels(p.arange(-90,90,30))
# lat/lon coordinates of five cities.
lats=[40.02,32.73,38.55,48.25,17.29]
lons=[-105.16,-117.16,-77.00,-114.21,-88.10]
cities=['Boulder, CO','San Diego, CA',
'Washington, DC','Whitefish, MT','Belize City, Belize']
# compute the native map projection coordinates for cities.
x,y = map(lons,lats)
# plot filled circles at the locations of the cities.
map.plot(x,y,'bo')
# plot the names of those five cities.
for name,xpt,ypt in zip(cities,x,y):
p.text(xpt+50000,ypt+50000,name,fontsize=9)
# make up some data on a regular lat/lon grid.
nlats = 73; nlons = 145; delta = 2.*p.pi/(nlons-1)
lats = (0.5*p.pi-delta*p.indices((nlats,nlons))[0,:,:])
lons = (delta*p.indices((nlats,nlons))[1,:,:])
wave = 0.75*(p.sin(2.*lats)**8*p.cos(4.*lons))
mean = 0.5*p.cos(2.*lats)*((p.sin(2.*lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons*180./p.pi, lats*180./p.pi)
# contour data over the map.
cs = map.contour(x,y,wave+mean,15,linewidths=1.5)
p.show()
#p.savefig('wiki_example.ps')
map.drawmeridians(p.arange(0, 360, 30))
map.drawparallels(p.arange(-90, 90, 30))
# lat/lon coordinates of five cities.
lats = [40.02, 32.73, 38.55, 48.25, 17.29]
lons = [-105.16, -117.16, -77.00, -114.21, -88.10]
cities = [
'Boulder, CO', 'San Diego, CA', 'Washington, DC', 'Whitefish, MT',
'Belize City, Belize'
]
# compute the native map projection coordinates for cities.
x, y = map(lons, lats)
# plot filled circles at the locations of the cities.
map.plot(x, y, 'bo')
# plot the names of those five cities.
for name, xpt, ypt in zip(cities, x, y):
p.text(xpt + 50000, ypt + 50000, name, fontsize=9)
# make up some data on a regular lat/lon grid.
nlats = 73
nlons = 145
delta = 2. * p.pi / (nlons - 1)
lats = (0.5 * p.pi - delta * p.indices((nlats, nlons))[0, :, :])
lons = (delta * p.indices((nlats, nlons))[1, :, :])
wave = 0.75 * (p.sin(2. * lats)**8 * p.cos(4. * lons))
mean = 0.5 * p.cos(2. * lats) * ((p.sin(2. * lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons * 180. / p.pi, lats * 180. / p.pi)
# contour data over the map.
cs = map.contour(x, y, wave + mean, 15, linewidths=1.5)
p.show()
#p.savefig('wiki_example.ps')
return img
img = cropImage()
# read the cropped image directly into a numpy array
imarray = numpy.array(img)
print "Numpy array shape:", imarray.shape
print "Cropped image size", img.size
# Create the gaussian data
data = imarray
pylab.matshow(data, cmap='gist_rainbow')
params = fa.fitgaussian(data)
fit = fa.gaussian(*params)
pylab.contour(fit(*pylab.indices(data.shape)), cmap='copper')
ax = pylab.gca()
(height, x, y, width_x, width_y) = params
legend = """
x : {:.1f}
y : {:.1f}
width_x : {:.1f}
width_y : {:.1f}""".format(x, y, width_x, width_y)
pylab.text(x=0.95,
y=0.05,
s=legend,
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
from pylab import zeros, ones, indices, uint8
from PIL import Image
rows = 1024
patterns = 7
data = zeros((patterns, rows, rows))
for i in range(patterns):
divisions = 2 ** (i + 1)
step = rows / divisions
period = 2 * step
data[i] = ones((rows, rows)) * ((indices((rows, rows))[1] % period) > step)
img = Image.fromarray(255 * data[i].astype(uint8), mode='L')
img.save("output/pattern_{}.png".format(i + 1))